We achieved state-of-the-art results in terms of counting errors in both datasets, also ensuring high Precision and Recall values. The Generic Matching Network (GMN) presented in [1]} and tested on the CARPK dataset has the advantage of being class agnostic. However, the error obtained using only three training images is about three times larger than the one obtained with our approach. The error using the entire training dataset is instead about two times larger than the one we got, and in this case, the benefits of using a class agnostic solution are less self-evident. The VGG-GAP-HR solution [2]} has instead the advantage of needing a dot-annotated training dataset, while our solution needs bounding box labels. Dot-annotation is a less time-consuming operation than the bounding box one, but the error of our solution is about half for both datasets.
Finally, comparing our approach with the one proposed by [3]} in terms of images processed per second, it is notable that the algorithm in [3]} is faster than our (14 fps instead of 4 fps). Still, the error in [3]} is pretty larger in both the exploited benchmarks compared to ours.
Finally, concerning the cross-dataset experiments, we experienced a performance degradation due to the overfitting over a specific scenario; however, counting errors remain comparable with the ones obtained using most of the other approaches, thus showing capabilities by our solution to generalize to new contexts.
